Skip to main content
SpringerLink
Log in

Automation Techniques in Aerobic Bacteriology

  • Chapter
  • First Online:
Automated Diagnostic Techniques in Medical Microbiology

  • 43 Accesses

Abstract

The automation in microbiology has been introduced some 30 years back. Major advances in technology over few decades led to the introduction of automated devices for sample inoculation, transportation to smart incubators, followed by its digital reading. The automation in aerobic bacteriology began first with the launch of first generation automated specimen processors, which took place more than 20 years ago. The various automated sample inoculation instruments now available are the Innova (BD), the InoqulA (BD Kiestra), the Previ-Isola (bio- Mérieux), the WASP (Copan) and the BD Phoenix AP (BD). Quite a number of automated aerobic bacterial ID and AST systems are also available in market. FDA cleared automated systems for phenotypic identification of bacteria includes: Vitek −2 (Biomerieux), MALDI-TOF MS (Biomerieux), MicroScan (Siemens Healthcare Diagnostics), Phoenix (BD Diagnostics) and Aris 2X (Sensititre, Thermo Fisher). For complete colony counting, two types of automatic colony counters are available, first are the digital counters which are not automatic in complete sense. It is dependent on the technician to identify and probe the colony. Another one is the semi- automatic or automatic counters. These are equipped with hardware to capture high quality images. Automation in blood culture system was one of the very early automations to be seen in the field of microbiology. Automated blood culture system was first developed in 1970. Currently three automated microbial detection systems have been approved for use in the detection of bacteremia, namely the BacT/Alert 3D (bioMerieux), BD Bactec Plus (BD) and VersaTREK (TREK diagnostics system). Studies have shown that automated systems have been successful in increasing the sample screening rates by around 19% for positive stool specimens, detection of MRSA, VRE and ESBL producing microorganisms has increased by around 2%, 5% and 5% respectively in various clinical specimens. Studies are also needed to assess the financial, operational and clinical impact of automation in microbiology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
eBook
USD 119.00
Price excludes VAT (USA)
Hardcover Book
USD 159.99
Price excludes VAT (USA)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. https://www.news-medical.net/life-sciences/Laboratory-Automation-in-Microbiology.aspx.

  2. Croxatto A, Prod'Hom G, Faverjon F, Rochais Y, Greub G. Laboratory automation in clinical bacteriology: what system to choose? Clin Microbiol Infect. 2016;22(3):217–35.

    Article  CAS  PubMed  Google Scholar 

  3. Dauwalder O, Landrieve L, Laurent F, De Montclos M, Vandenesch F, Lina G. Does bacteriology laboratory automation reduce time to results and increase quality management? Clin Microbiol Infect. 2016;22(3):236–43.

    Article  CAS  PubMed  Google Scholar 

  4. Bourbeau PP, Ledeboer NA. Automation in clinical microbiology. J Clin Microbiol. 2013;51(6):1658–65.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Croxatto A, Dijkstra K, Prod'hom G, Greub G. Comparison of inoculation with the InoqulA and WASP automated systems with manual inoculation. J Clin Microbiol. 2015;53(7):2298–307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Mischnik A, Mieth M, Busch CJ, Hofer S, Zimmermann S. First evaluation of automated specimen inoculation for wound swab samples by use of the Previ Isola system compared to manual inoculation in a routine laboratory: finding a cost-effective and accurate approach. J Clin Microbiol. 2012;50(8):2732–6.

    Article  PubMed  PubMed Central  Google Scholar 

  7. https://assets.thermofisher.com/TFS-Assets/MBD/brochures/COPAN-WASPLab-Full-Laboratory-Automation-brochure.pdf.

  8. Gao J, Chen Q, Peng Y, Jiang N, Shi Y, Ying C. Copan walk away specimen processor (WASP) automated system for pathogen detection in female reproductive tract specimens. Frontiers in cellular and infection. Microbiology. 2021;11:11.

    Google Scholar 

  9. Bourbeau PP, Swartz BL. First evaluation of the WASP, a new automated microbiology plating instrument. J Clin Microbiol. 2009;47(4):1101–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Froment P, Marchandin H, Vande Perre P, Lamy B. Automated versus manual sample inoculations in routine clinical microbiology: a performance evaluation of the fully automated InoqulA instrument. J Clin Microbiol. 2014;52(3):796–802.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. O'hara CM. Manual and automated instrumentation for identification of Enterobacteriaceae and other aerobic gram-negative bacilli. Clin Microbiol Rev. 2005;18(1):147–62.

    Article  PubMed  PubMed Central  Google Scholar 

  12. https://basicmedicalkey.com/3-rapid-devices-and-instruments-for-the-identification-of-aerobic-bacteria/.

  13. Isenberg HD, Gavan TL, Smith PB, Sonnenwirth A, Taylor W, Martin WJ, Rhoden D, Balows A. Collaborative investigation of theAutoMicrobic system Enterobacteriaceae biochemical card. J Clin Microbiol. 1980;11:694–702.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Santhosh S, Kamaraj R. 510 (k) premarket notification. Res J Pharm Technol. 2018;11(12):5675–80.

    Article  Google Scholar 

  15. Pincus DH. Microbial identification using the bioMérieux Vitek® 2 system. In: Encyclopedia of rapid microbiological methods. Bethesda, MD: Parenteral Drug Association; 2006. p. 1–32.

    Google Scholar 

  16. Kalorama United States Market for In Vitro Diagnostic Tests. 2017. 878.

    Google Scholar 

  17. https://www.beckmancoulter.com/en/products/microbiology/dxm-microscan-walkaway-system#/specifications.

  18. Bulik CC, Fauntleroy KA, Jenkins SG, Abuali M, LaBombardi VJ, Nicolau DP, Kuti JL. Comparison of meropenem MICs and susceptibilities for carbapenemase-producing Klebsiella pneumoniae isolates by various testing methods. J Clin Microbiol. 2010;48(7):2402–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Anderson KF, Lonsway DR, Rasheed JK, Biddle J, Jensen B, McDougal LK, Carey RB, Thompson A, Stocker S, Limbago B, Patel JB. Evaluation of methods to identify the Klebsiella pneumoniae carbapenemase in Enterobacteriaceae. J Clin Microbiol. 2007;45(8):2723–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Woodford N, Eastaway AT, Ford M, Leanord A, Keane C, Quayle RM, Steer JA, Zhang J, Livermore DM. Comparison of BD Phoenix, Vitek 2, and MicroScan automated systems for detection and inference of mechanisms responsible for carbapenem resistance in Enterobacteriaceae. J Clin Microbiol. 2010;48(8):2999–3002.

    Article  PubMed  PubMed Central  Google Scholar 

  21. https://www.rapidmicrobiology.com/archived-news?id=2762h1.

  22. https://microbeonline.com/maldi-tof-ms-principle-applications-microbiology/.

  23. Marvin LF, Roberts MA, Fay LB. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry in clinical chemistry. Clin Chim Acta. 2003;337(1–2):11–21.

    Article  CAS  PubMed  Google Scholar 

  24. Clark AE, Kaleta EJ, Arora A, Wolk DM. Matrix-assisted laser desorption ionization–time of flight mass spectrometry: a fundamental shift in the routine practice of clinical microbiology. Clin Microbiol Rev. 2013;26(3):547–603.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Vega-Rúa A, Fontaine A, Nuccio C, Hery L, Goindin D, Gustave J, Almeras L. Improvement of mosquito identification by MALDI-TOF MS biotyping using protein signatures from two body parts. Parasit Vectors. 2018;11(1):1–2.

    Article  Google Scholar 

  26. Rychert J. Benefits and limitations of MALDI-TOF mass spectrometry for the identification of microorganisms. J Infect Epidemiol. 2019;2(4):1–5.

    Google Scholar 

  27. Croxatto A, Prod'hom G, Greub G. Applications of MALDI-TOF mass spectrometry in clinical diagnostic microbiology. FEMS Microbiol Rev. 2012;36(2):380–407.

    Article  CAS  PubMed  Google Scholar 

  28. Chandler LJ, LaSala PR, Whittier S. Rapid devices and instruments for the identification of aerobic bacteria. In: Manual of commercial methods in clinical microbiology: international edition. Hoboken, NJ: Wiley; 2016. p. 21–55.

    Chapter  Google Scholar 

  29. Junkins AD, Arbefeville SS, Howard WJ, Richter SS. Comparison of BD Phoenix AP workflow with Vitek 2. J Clin Microbiol. 2010;48(5):1929–31.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Snyder JW, Munier GK, Johnson CL. Direct comparison of the BD phoenix system with the MicroScan WalkAway system for identification and antimicrobial susceptibility testing of Enterobacteriaceae and nonfermentative gram-negative organisms. J Clin Microbiol. 2008;46(7):2327–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Funke G, Funke-Kissling P. Use of the BD PHOENIX automated microbiology system for direct identification and susceptibility testing of gram-negative rods from positive blood cultures in a three-phase trial. J Clin Microbiol. 2004;42(4):1466–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. O'Hara CM. Evaluation of the Phoenix 100 ID/AST system and NID panel for identification of Enterobacteriaceae, Vibrionaceae, and commonly isolated nonenteric gram-negative bacilli. J Clin Microbiol. 2006;44(3):928–33.

    Article  PubMed  PubMed Central  Google Scholar 

  33. https://www.rapidmicrobiology.com/news/new-smaller-footprint-bd-phoenix-m50-idast-system.

  34. Analyte C, Proprietary F. 510 (k) Substantial equivalence determination decision summary device only template.

    Google Scholar 

  35. https://www.bd.com/en-us/products-and-solutions/products/product-families/bd-phoenix-m50-instrument.

  36. https://tools.thermofisher.com/content/sfs/brochures/Sensititre%20Brochure_EN.pdf.

  37. Chen WB, Zhang C. An automated bacterial colony counting and classification system. Inf Syst Front. 2009;11(4):349–68.

    Article  Google Scholar 

  38. Zhang C, Chen WB, Liu WL, Chen CB. An automated bacterial colony counting system. In2008 IEEE international conference on sensor networks, ubiquitous, and trustworthy computing (sutc 2008). Mumbai: IEEE; 2008. p. 233–40.

    Google Scholar 

  39. Niyazi M, Niyazi I, Belka C. Counting colonies of clonogenic assays by using densitometric software. Radiat Oncol. 2007;2(1):1–3.

    Article  Google Scholar 

  40. Murray PR, Masur H. Current approaches to the diagnosis of bacterial and fungal bloodstream infections for the ICU. Crit Care Med. 2012;40(12):3277–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. https://www.ijmronline.org/html-article/15432.

  42. Brunkhorst FM, Seifert H, Kaasch A, Welte T. Shortfalls in the application of blood culture testing in ICU patients with suspected sepsis. DIVI. 2010;1:23.

    Google Scholar 

  43. Levy MM, Dellinger RP, Townsend SR, Linde-Zwirble WT, Marshall JC, Bion J, Schorr C, Artigas A, Ramsay G, Beale R, Parker MM. The surviving sepsis campaign: results of an international guideline-based performance improvement program targeting severe sepsis. Intensive Care Med. 2010;36(2):222–31.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Li G, Sun J, Pan S, Li W, Zhang S, Wang Y, Sun X, Xu H, Ming L. Comparison of the performance of three blood culture systems in a Chinese tertiary-care hospital. Front Cell Infect Microbiol. 2019;9:285.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. BD BACTEC™ 9000 Systems: A World of Difference in Blood Culture.

    Google Scholar 

  46. Bourbeau P, Riley J, Heiter BJ, Master R, Young C, Pierson C. Use of the BacT/alert blood culture system for culture of sterile body fluids other than blood. J Clin Microbiol. 1998;36(11):3273–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. https://assets.thermofisher.com/TFS-Assets/MBD/brochures/VersaTREK-Brochure-EN.pdf.

Download references

Author information

Authors and Affiliations

  1. Department of Microbiology, Dr. Ram Manohar Lohia Institute of Medical Sciences, Lucknow, India

    Akanksha Gupta & Jyotsna Agarwal

Authors
  1. Akanksha Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jyotsna Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Biotechnology, Faculty of Allied Health Sciences, Mahayogi Gorakhnath University, Gorakhpur, Uttar Pradesh, India

    Sunil Kumar

  2. Department of Biotechnology, National Institute of Technology, Raipur, Chhattisgarh, India

    Awanish Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gupta, A., Agarwal, J. (2024). Automation Techniques in Aerobic Bacteriology. In: Kumar, S., Kumar, A. (eds) Automated Diagnostic Techniques in Medical Microbiology. Springer, Singapore. https://doi.org/10.1007/978-981-99-9943-9_3

Download citation

Publish with us

Policies and ethics

Search

Navigation